Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Explainability as statistical inference
Authors: Hugo Henri Joseph Senetaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose new datasets with ground truth selection which allow for the evaluation of the features importance map and show experimentally that multiple imputation provides more reasonable interpretations. |
| Researcher Affiliation | Academia | 1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark 2Universit e CΛote d Azur, Inria, Maasai, LJAD, CNRS, Nice, France. Correspondence to: Hugo Henri Joseph Senetaire <EMAIL>. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block found. |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology described in this paper. |
| Open Datasets | Yes | For each dataset, we generate 5 different datasets containing each 10,000 train samples and 10, 000 test samples. |
| Dataset Splits | Yes | The split between train and validation is split randomly with proportion 80%, 20%. Hence, the train dataset of the switching panels input contain 48,000 images, the validation dataset contains 12,000 images and the test dataset 10,000 images. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions software components like "Adam", "U-Net", "Quickshift", "SHAP", "FASTSHAP", "Sklearn", but does not provide specific version numbers for these or other key software dependencies. |
| Experiment Setup | Yes | We trained all the methods for 1000 epochs using Adam for optimisation with a learning rate 10 4 and weight decay 10 3 with a batch size of 1000. |